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CF threejs-volumetric-clouds

Implement volumetric cloud systems in Three.js. Use for weather-driven density, bounded raymarching, shape/detail erosion, vertical profiles, lighting cones, silver lining, temporal reconstruction, cloud shadows, multiple layers, and scalable quality modes.

scottstts/Threejs-Awesome-Graphics-Agent-Skills Agent Skills author: scottstts MIT 80 files · 77 scripts body ≈ 567 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Implement volumetric cloud systems in Three.js. Use for weather-driven density, bounded raymarching, shape/detail erosion, vertical profiles, lighting cones…

As a process F 40/100 · Will not run — References files that are not bundled: references/weather-volume-and-reconstruction.md

Proceduretype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
C
87/100
safety, quality, tests
Safety 60%
96
Quality 40%
74
Run on models
none yet
Process rating
F
40/100
Will not run
References files that are not bundled: references/weather-volume-and-reconstruction.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 4

✓ No critical or high findings

Medium and low: 4
  • low Secrets in code secret-high-entropy-token examples/weather-volume-clouds/source/clouds/Procedural3DTexture.ts:21
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
    export interface Proc…ers {
    fixture
  • low Secrets in code secret-high-entropy-token examples/weather-volume-clouds/source/clouds/Procedural3DTexture.ts:35
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
    constructor({ size, fragmentShader }: Proc…ers) {
    fixture
  • low Secrets in code secret-high-entropy-token examples/weather-volume-clouds/source/geospatial/DataLoader.ts:138
    High-entropy token-like string (may be an id, hash or a credential) (placeholder value)
    export function crea…ass<T extends TypedArray>(
    placeholder
  • low Secrets in code secret-high-entropy-token examples/weather-volume-clouds/source/geospatial/DataLoader.ts:156
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
    return new (crea…ass(parser, parameters))()
    fixture

Files scanned: 80. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/weather-volume-and-reconstruction.md

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: references/weather-volume-and-reconstruction.md
  • 0Tools and files. 1 referenced file(s) missing: references/weather-volume-and-reconstruction.md
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 20 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 567 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 257: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 20 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.